More Like this

1. The NEON Daily Isotopic Composition of Environmental Exchanges Dataset

   https://doi.org/10.1038/s41597-022-01412-4  

   Finkenbiner, Catherine E.; Li, Bonan; Spencer, Lindsey; Butler, Zachariah; Haagsma, Marja; Fiorella, Richard P.; Allen, Scott T.; Anderegg, William; Still, Christopher J.; Noone, David; et al (December 2022, Scientific Data)

   Abstract The National Ecological Observatory Network (NEON) provides open-access measurements of stable isotope ratios in atmospheric water vapor (δ 2 H, δ 18 O) and carbon dioxide (δ 13 C) at different tower heights, as well as aggregated biweekly precipitation samples (δ 2 H, δ 18 O) across the United States. These measurements were used to create the NEON Daily Isotopic Composition of Environmental Exchanges (NEON-DICEE) dataset estimating precipitation (P; δ 2 H, δ 18 O), evapotranspiration (ET; δ 2 H, δ 18 O), and net ecosystem exchange (NEE; δ 13 C) isotope ratios. Statistically downscaled precipitation datasets were generated to be consistent with the estimated covariance between isotope ratios and precipitation amounts at daily time scales. Isotope ratios in ET and NEE fluxes were estimated using a mixing-model approach with calibrated NEON tower measurements. NEON-DICEE is publicly available on HydroShare and can be reproduced or modified to fit user specific applications or include additional NEON data records as they become available. The NEON-DICEE dataset can facilitate understanding of terrestrial ecosystem processes through their incorporation into environmental investigations that require daily δ 2 H, δ 18 O, and δ 13 C flux data. 

   more » « less
2. Coupled machine learning–ecosystem ensemble models substantially improve predictions of nitrous oxide (N ₂ O) fluxes from US croplands

   https://doi.org/10.1073/pnas.2524808123  

   Sharma, Prateek; Basso, Bruno; Manuraj, Aditya; Murillo, Michael S; Millar, Neville; Tadiello, Tommaso; Sharma, Mukta; Delandmeter, Mathieu; Robertson, G Philip (March 2026, Proceedings of the National Academy of Sciences)

   Nitrous oxide (N₂O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N₂O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high-emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N₂O fluxes from US cropland. Trained and validated on ~12,000 N₂O chamber measurements at 17 US Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N₂O at both training (R²= 0.84, RMSE = 16.4 g N ha⁻¹d⁻¹) and held-out testing sites (R²= 0.84, RMSE = 6.2 g N ha⁻¹d⁻¹). Analyses identified six dominant N₂O drivers: soil organic carbon (SOC), NH₄⁺, NO₃^-, water-filled pore space, temperature, and aboveground biomass production. Wet, warm soils produced large N₂O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N₂O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops. 

   more » « less
3. High-Resolution Mapping of Soil Organic Carbon Under Different Land-Use Types Using NEON Remote Sensing Data.

   Sihi, D; Gelsleichter, YA; Konduri, VS; Wang, Z; Kumar, J; Weintraub-Leff, SR (November 2024, ASA, CSSA, SSSA International Annual Meeting, San Antonio, TX. https://scisoc.confex.com/scisoc/2024am/meetingapp.cgi/Paper/160215)

   Soil organic carbon (SOC) represents the largest terrestrial carbon pool. Effectively monitoring SOC at high spatial resolution is crucial for estimating carbon budgets at the ecosystem scale and informing climate change mitigation efforts at the regional scale. Traditional soil sampling methods, however, are laborious and expensive. Remote sensing platforms can be used to survey large landscapes to meet the need for rapid and cost-effective approaches for quantifying SOC at landscape to regional scales, if relationships between remotely sensed variables and SOC can be established. We developed a workflow to analyze and predict SOC content based on National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) remote sensing data. First, we benchmarked related tools and developed reproducible workflows using NEON remote sensing datasets. Hyperspectral data were extracted from the locations where NEON soil data exist. Additional variables from the LiDAR data and key metadata (climate and land cover) were extracted for those locations. Random Forest and Partial Least Squares Regression techniques were then used to create models for fine-scale SOC prediction. Cross-validation was embedded in the model creation step. The most important covariates were selected through recursive feature elimination, stepwise selection, and expert judgment. Preliminary results indicate that machine learning models can re-produce SOC measurements in testing datasets. Key predictors include topographic variables, vegetation indices, and specific wavelength bands in hyperspectral images. We are further validating our algorithms using SOC data from ISCN (International Soil Carbon Network) and SoDaH (SOils DAta Harmonization database) that are co-located with NEON sites. We are creating high-resolution SOC maps for 0-30 cm depth at NEON sites and testing our algorithms for different land use types. Our work paves the way for a broader assessment of SOC stocks using remote sensing observations, and our high-resolution SOC maps will potentially help quantify carbon budgets across heterogeneous landscapes. 

   more » « less
4. FLUXNET-CH ₄ Synthesis Activity: Objectives, Observations, and Future Directions

   https://doi.org/10.1175/BAMS-D-18-0268.1  

   Knox, Sara H.; Jackson, Robert B.; Poulter, Benjamin; McNicol, Gavin; Fluet-Chouinard, Etienne; Zhang, Zhen; Hugelius, Gustaf; Bousquet, Philippe; Canadell, Josep G.; Saunois, Marielle; et al (December 2019, Bulletin of the American Meteorological Society)

   This paper describes the formation of, and initial results for, a new FLUXNET coordination network for ecosystem-scale methane (CH 4 ) measurements at 60 sites globally, organized by the Global Carbon Project in partnership with other initiatives and regional flux tower networks. The objectives of the effort are presented along with an overview of the coverage of eddy covariance (EC) CH 4 flux measurements globally, initial results comparing CH 4 fluxes across the sites, and future research directions and needs. Annual estimates of net CH 4 fluxes across sites ranged from −0.2 ± 0.02 g C m –2 yr –1 for an upland forest site to 114.9 ± 13.4 g C m –2 yr –1 for an estuarine freshwater marsh, with fluxes exceeding 40 g C m –2 yr –1 at multiple sites. Average annual soil and air temperatures were found to be the strongest predictor of annual CH 4 flux across wetland sites globally. Water table position was positively correlated with annual CH 4 emissions, although only for wetland sites that were not consistently inundated throughout the year. The ratio of annual CH 4 fluxes to ecosystem respiration increased significantly with mean site temperature. Uncertainties in annual CH 4 estimates due to gap-filling and random errors were on average ±1.6 g C m –2 yr –1 at 95% confidence, with the relative error decreasing exponentially with increasing flux magnitude across sites. Through the analysis and synthesis of a growing EC CH 4 flux database, the controls on ecosystem CH 4 fluxes can be better understood, used to inform and validate Earth system models, and reconcile differences between land surface model- and atmospheric-based estimates of CH 4 emissions. 

   more » « less
5. Quantifying the Relative Importance of Sand Deposition and Dune Grasses to Carbon Storage in US Central Atlantic Coast Dunes

   https://doi.org/10.1007/s12237-025-01484-6  

   Jay, Katya R; Hacker, Sally D; Hagen, Cedric J; Stepanek, John; Moore, Laura J; Ruggiero, Peter (May 2025, Estuaries and Coasts)

   Abstract Coastal ecosystems such as mangroves, salt marshes, and seagrasses sequester large amounts of carbon per unit area due to their high productivity and sediment accumulation rates. However, only a handful of studies have examined carbon sequestration in coastal dunes, which are shaped by biophysical feedback between aeolian sediment transport and burial-tolerant vegetation. The goal of this study was to measure carbon storage and identify the factors that influence its variability along the foredunes of the US Outer Banks barrier islands of North Carolina. Specifically, differences in carbon stocks (above- and belowground biomass and sand), dune grass abundance, and sand supply were measured among islands, cross-shore dune profile locations, and dune grass species. Carbon varied among aboveground grass biomass (0.1 ± 0.1 kg C m⁻²), belowground grass biomass (1.1 ± 1.6 kg C m⁻³), and sand (0.9 ± 0.6 kg C m⁻³), with the largest amount in belowground grass stocks. Aboveground grass carbon stocks were comparable to those in eelgrass beds and salt marshes on a per-area basis, while sediment carbon values in our study system were lower than those in other coastal systems, including other dune locations. Additionally, sand carbon density was positively related to patterns in dune sand supply and grass abundance, reflecting a self-reinforcing vegetation-sediment feedback at both high and low sand accumulation rates. 

   more » « less